__author__ = 'Stephanie Juneau, NOAO Data Lab Team'
__version__ = '20171219' # yyyymmdd
__datasets__ = ['des_dr1'] # enter used datasets by hand;
# for a list of available datasets, see cell "Available datasets in Data Lab" further below
Star/Galaxy/QSO Classification in the Dark Energy Survey (DES)¶
by Stéphanie Juneau, Knut Olsen, Robert Nikutta and the NOAO Data Lab Team
In this notebook, we investigate the optical colors of astronomical sources detected in the Dark Energy Survey (DES), an imaging survey conducted with the Dark Energy Camera (DECam) on the 4-meter Blanco telescope. The first data release, DR1, comprises ~400 millions stars, galaxies and quasars (or QSOs: Quasi-Stellar Objects).
NOAO Data Lab products and services used here:
- the DES DR1 database
- Jupyter Notebook Server
- Query Manager
Below, we query the database, compute colors, plot a few color combinations, and take into account the source shapes as defined from the spread of the light profiles and a pre-computed star-galaxy classifier, in order to differentiate between object classes.
0. Disclaimer & attribution¶
If you use this notebook for your published science, please acknowledge the following:
Data Lab concept paper: Fitzpatrick et al., "The NOAO Data Laboratory: a conceptual overview", SPIE, 9149, 2014, http://dx.doi.org/10.1117/12.2057445
Data Lab disclaimer: http://datalab.noao.edu/known-issues.php
1. Introduction¶
An imaging survey includes a zoo of different astronomical objects. There are foreground stars from our own Milky Way galaxies, and background galaxies at various distances, including QSOs (quasi-stellar objects) with actively accreting supermassive black holes. In this Notebook, we aim to distinguish between object classes using combinations of colors and shape parameters.
1.1 Magnitudes and Colors¶
Magnitudes are obtained through a set of filters similar to the u,g,r,i,z set used for SDSS. In this work, we use g,r,z from the Dark Energy Camera (DECam)
Colors are defined as a difference between magnitudes in two bands. A "redder" color means that the object is comparatively brighter in the redder (i.e., longer wavelength) band. Conversely, a "bluer" color means that the object is comparatively brighther in the bluer (i.e., shorter wavelenght) band.
We will use the following colors:
- *g-r*
- *r-z*
1.2 Object Shapes/Types¶
The object shape (2D light profile) can be defined with a size and ellipticity. In this Notebook, we are interested in distinguishing between stars as point sources, and galaxies as extended sources.
- *kron_radius*: Kron radius in pixels
- *spread_model_X*: star-galaxy classifier comparing extended model to PSF model. Value is given for each band X = g, r, i, z, y
- *class_star_X*: value ranging from 0.0 (not a star) to 1.0 (star), for each band X = g, r, i, z, y
The value of spread_model is near zero for a point source (star or QSO), positive for an extended source (galaxy), negative for an artefact smaller than the PSF (e.g., bad pixel or cosmic ray). More information about the spread_model can be found in Desai et al (2012), and Soumagnac et al (2015).
2. Imports and setup¶
# Python 2/3 compatibility
from __future__ import print_function # to use print() as a function in Python 2
#from __future__ import division # if you need Python 3 division behavior in Python 2
try:
input = raw_input # use 'input' function in both Python 2 and 3
except NameError:
pass
# std lib
from getpass import getpass
from cStringIO import StringIO #C script to handle string format
# 3rd party
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.ticker import NullFormatter
from astropy.utils.data import download_file #import file from URL
from scipy.stats import binned_statistic_2d
%matplotlib inline
from astropy.table import Table
# Data Lab
from dl import authClient as ac, queryClient as qc, storeClient as sc, helpers
print('Done importing')
3. Authentication¶
# Python 2/3 compatibility
try:
input = raw_input
except NameError:
pass
# Either get token for anonymous user
token = ac.login('anonymous')
# ... or for authenticated user
#token = ac.login(input("Enter user name: "),getpass("Enter password: "))
4. Available datasets in Data Lab¶
# set default profile
qc.set_profile('default')
# these schemas are not astronomical datasets
_remove = set(['ivao','ivao_smash','tap_schema','schema'])
# get all schemas from DB
_schemas = set(qc.query(token,sql='SELECT schema FROM tbl_stat').split())
# remove non-astro schemas
_alldatasets = sorted(list(_schemas - _remove))
print("Datasets available in Data Lab (with current profile):\n", _alldatasets)
# TODO: Enter used datasets by hand in the list __datasets__ in the very first cell above
5. Query DES Photometry Catalog¶
The photometry is derived from Tractor modeling of sources, and the database includes model photometry, type (shape), as well as aperture photometry in various aperture sizes. In this work, we will use Tractor model magnitudes.
The database is called des_dr1 and includes several tables. We will use the main table. The column names and descriptions can be found from the Data Lab Query Interface or using the Table Access Protocol (TAP) service with, e.g., TOPCAT. In both cases, we are interested in des_dr1.main.
# Before DES public release: set profile to use non-public version of DES
qc.set_profile('des-proto')
print(qc.schema('des_dr1',format='json',profile='des-proto'))
# Write query statement (sql) as a string
# NOTE: triple quotes allow us to break the string on multiple lines
query = """
SELECT mag_auto_g as gmag, mag_auto_r as rmag, mag_auto_z as zmag,
mag_auto_i as imag, mag_auto_y as ymag,
kron_radius, spread_model_g, class_star_g, spread_model_r, class_star_r,
spread_model_z, class_star_z,
flux_auto_g/fluxerr_auto_g as snr_g2, snr_g, snr_r, snr_z, ra, dec
FROM des_dr1.main
WHERE (fluxerr_auto_g>0 and fluxerr_auto_r>0 and fluxerr_auto_z>0)
LIMIT 300000"""
# mag_auto_g,r,i,z,y = AB magnitudes in DECam g,r,i,z,y bands
# kron_radius = Kron radius from SExtractor (pixels)
# spread_model_g,r,z = star/galaxy classifier quantifying light profile relative to PSF
# class_star_g,r,z = star/extended source classifier (from 0 to 1)
# snr_g,r,z = computed signal-to-noise ratios (S/N) in g,r,z bands
# ra,dec = celestial coordinates
#
# WHERE: requirement that error>0 (to avoid dividing by zero) in g,r,z bands
# LIMIT: returns 200,000 rows that satisfy the query
print(query)
%%time
# Execute the Query synchronously if short (e.g., LIMIT<=100,000)
response = qc.query(token, sql=query, fmt='csv')
print('Time')
# Reformat output into a Pandas Data Frame
df = helpers.convert(response,'pandas')
df_all = df
# Print a few rows from the result data frame
print(df[:5])
len(df)
6. Plot Results¶
6.1 Optical Color-Color Diagram¶
# Select range of interest
thres = 5. #threshold value for S/N (here, making it more stringent than query)
keep = (df['snr_g']>thres)&(df['snr_r']>thres)&(df['snr_z']>thres)
# Colors
g_r = df['gmag'][keep] - df['rmag'][keep]
r_z = df['rmag'][keep] - df['zmag'][keep]
r_i = df['rmag'][keep] - df['imag'][keep]
i_z = df['imag'][keep] - df['zmag'][keep]
z_y = df['zmag'][keep] - df['ymag'][keep]
len(g_r)
# Classification per object type
#objtype = result['type'][keep]
col0 = r_z #r-z color
col1 = g_r #g-r color
# 2D-histogram of objects
fig, ax1 = plt.subplots(1, 1, figsize=(9, 8))
im1 = ax1.hexbin(col0, col1, bins='log', cmap=plt.cm.viridis,
mincnt=1, extent=(-1., 3, -1., 3))
ax1.set_xlabel('r-z',fontsize=20)
ax1.set_ylabel('g-r',fontsize=20)
#color bar
cb = plt.colorbar(im1,label='log(N)')
6.2 Morphological Parameters¶
g band¶
# invalid mags are set to 99 -> mag_auto_X < 90
# spread_model_X is valid at abs(spread_model)<1
# require S/N>5 in the g band
df=df[(df['gmag']<90) & (df['imag']<90) & (df['spread_model_g']>(-1)) & \
(df['rmag']<90) & (df['zmag']<90) & (df['spread_model_g']<1) & \
((df['snr_g'])>5)]
fig = plt.figure(figsize=(8*2,8*3))
ax = fig.add_subplot(3,2,1)
ax.hexbin(df['gmag'],df['spread_model_g'],gridsize=200,bins='log',mincnt=1,extent=(12,26,-0.05,0.05))
ax.set_xlabel('g',fontsize=20)
ax.set_ylabel('spread_model_g',fontsize=20)
ax.tick_params(labelsize=10)
ax2 = fig.add_subplot(3,2,2)
ax2.hexbin(df['kron_radius'],df['spread_model_g'],gridsize=200,bins='log',mincnt=1,extent=(0,10,-0.1,0.1))
ax2.set_xlabel('Kron radius',fontsize=20)
ax2.set_ylabel('spread_model_g',fontsize=20)
ax2.tick_params(labelsize=10)
ax3 = fig.add_subplot(3,2,3)
ax3.hexbin(df['gmag'],df['kron_radius'],gridsize=200,bins='log',mincnt=1,extent=(12,26,0,10))
ax3.set_xlabel('g',fontsize=20)
ax3.set_ylabel('Kron radius',fontsize=20)
ax3.tick_params(labelsize=10)
ax4 = fig.add_subplot(3,2,4)
ax4.hexbin(df['kron_radius'],df['class_star_g'],gridsize=200,bins='log',mincnt=1,extent=(0,10,0,1))
ax4.set_xlabel('Kron radius',fontsize=20)
ax4.set_ylabel('Class_star_g',fontsize=20)
ax4.tick_params(labelsize=10)
ax5 = fig.add_subplot(3,2,5)
ax5.hexbin(df['gmag'],df['class_star_g'],gridsize=200,bins='log',mincnt=1,extent=(12,26,0,1))
ax5.set_xlabel('g',fontsize=20)
ax5.set_ylabel('Class_star_g',fontsize=20)
ax5.tick_params(labelsize=10)
ax6 = fig.add_subplot(3,2,6)
ax6.hexbin(df['spread_model_g'],df['class_star_g'],gridsize=200,bins='log',mincnt=1,extent=(-0.025,0.025,0,1))
ax6.set_xlabel('spread_model_g',fontsize=20)
ax6.set_ylabel('Class_star_g',fontsize=20)
ax6.tick_params(labelsize=10)
r band¶
# reset df
df = df_all
# invalid mags are set to 99 -> mag_auto_X < 90
# spread_model_X is valid at abs(spread_model)<1
# require S/N>5 in the r band
df=df[(df['gmag']<90) & (df['imag']<90) & (df['spread_model_r']>(-1)) & \
(df['rmag']<90) & (df['zmag']<90) & (df['spread_model_r']<1) & \
((df['snr_r'])>5)]
fig = plt.figure(figsize=(8*2,8*3))
ax = fig.add_subplot(3,2,1)
ax.hexbin(df['rmag'],df['spread_model_r'],gridsize=200,bins='log',mincnt=1,extent=(12,26,-0.05,0.05))
ax.set_xlabel('r',fontsize=20)
ax.set_ylabel('spread_model_r',fontsize=20)
ax.tick_params(labelsize=10)
ax2 = fig.add_subplot(3,2,2)
ax2.hexbin(df['kron_radius'],df['spread_model_r'],gridsize=200,bins='log',mincnt=1,extent=(0,10,-0.1,0.1))
ax2.set_xlabel('Kron radius',fontsize=20)
ax2.set_ylabel('spread_model_r',fontsize=20)
ax2.tick_params(labelsize=10)
ax3 = fig.add_subplot(3,2,3)
ax3.hexbin(df['rmag'],df['kron_radius'],gridsize=200,bins='log',mincnt=1,extent=(12,26,0,10))
ax3.set_xlabel('r',fontsize=20)
ax3.set_ylabel('Kron radius',fontsize=20)
ax3.tick_params(labelsize=10)
ax4 = fig.add_subplot(3,2,4)
ax4.hexbin(df['kron_radius'],df['class_star_r'],gridsize=200,bins='log',mincnt=1,extent=(0,10,0,1))
ax4.set_xlabel('Kron radius',fontsize=20)
ax4.set_ylabel('Class_star_r',fontsize=20)
ax4.tick_params(labelsize=10)
ax5 = fig.add_subplot(3,2,5)
ax5.hexbin(df['rmag'],df['class_star_r'],gridsize=200,bins='log',mincnt=1,extent=(12,26,0,1))
ax5.set_xlabel('r',fontsize=20)
ax5.set_ylabel('Class_star_r',fontsize=20)
ax5.tick_params(labelsize=10)
ax6 = fig.add_subplot(3,2,6)
ax6.hexbin(df['spread_model_r'],df['class_star_r'],gridsize=200,bins='log',mincnt=1,extent=(-0.025,0.025,0,1))
ax6.set_xlabel('spread_model_r',fontsize=20)
ax6.set_ylabel('Class_star_r',fontsize=20)
ax6.tick_params(labelsize=10)
z band¶
# reset df
df = df_all
# invalid mags are set to 99 -> mag_auto_X < 90
# spread_model_X is valid at abs(spread_model)<1
# require S/N>5 in the z band
df=df[(df['gmag']<90) & (df['imag']<90) & (df['spread_model_z']>(-1)) & \
(df['rmag']<90) & (df['zmag']<90) & (df['spread_model_z']<1) & \
((df['snr_z'])>5)]
fig = plt.figure(figsize=(8*2,8*3))
ax = fig.add_subplot(3,2,1)
ax.hexbin(df['zmag'],df['spread_model_z'],gridsize=200,bins='log',mincnt=1,extent=(12,26,-0.05,0.05))
ax.set_xlabel('z',fontsize=20)
ax.set_ylabel('spread_model_z',fontsize=20)
ax.tick_params(labelsize=10)
ax2 = fig.add_subplot(3,2,2)
ax2.hexbin(df['kron_radius'],df['spread_model_z'],gridsize=200,bins='log',mincnt=1,extent=(0,10,-0.1,0.1))
ax2.set_xlabel('Kron radius',fontsize=20)
ax2.set_ylabel('spread_model_z',fontsize=20)
ax2.tick_params(labelsize=10)
ax3 = fig.add_subplot(3,2,3)
ax3.hexbin(df['zmag'],df['kron_radius'],gridsize=200,bins='log',mincnt=1,extent=(12,26,0,10))
ax3.set_xlabel('z',fontsize=20)
ax3.set_ylabel('Kron radius',fontsize=20)
ax3.tick_params(labelsize=10)
ax4 = fig.add_subplot(3,2,4)
ax4.hexbin(df['kron_radius'],df['class_star_z'],gridsize=200,bins='log',mincnt=1,extent=(0,10,0,1))
ax4.set_xlabel('Kron radius',fontsize=20)
ax4.set_ylabel('Class_star_z',fontsize=20)
ax4.tick_params(labelsize=10)
ax5 = fig.add_subplot(3,2,5)
ax5.hexbin(df['zmag'],df['class_star_z'],gridsize=200,bins='log',mincnt=1,extent=(12,26,0,1))
ax5.set_xlabel('z',fontsize=20)
ax5.set_ylabel('Class_star_z',fontsize=20)
ax5.tick_params(labelsize=10)
ax6 = fig.add_subplot(3,2,6)
ax6.hexbin(df['spread_model_z'],df['class_star_z'],gridsize=200,bins='log',mincnt=1,extent=(-0.025,0.025,0,1))
ax6.set_xlabel('spread_model_z',fontsize=20)
ax6.set_ylabel('Class_star_z',fontsize=20)
ax6.tick_params(labelsize=10)
Combining bands¶
Above, the spread_model yields the best separation in the z band, as well as the fewest ridge features in distribution of class_star. On the other hand, the g band shows the most overlap in spread_model values at faint magnitudes, and the most striking ridges in the distribution of class_star. For testing purposes, let's see what happens for cases that have detections in all 3 g,r,z bands, and then when the classification agrees between the bands.
First, we plot again the "worst" case (g band) but requiring objects to have S/N>5 in all of g,r,z bands.
# reset df
df = df_all
# invalid mags are set to 99 but requiring S/N>5 will only keep valid mags
# spread_model_X is valid at abs(spread_model)<1
# require S/N>5 in the g, r, z band
df=df[(df['spread_model_g']>(-1)) & (df['spread_model_g']<1) & \
(df['snr_g']>5) & (df['snr_r']>5) & (df['snr_z']>5)]
fig = plt.figure(figsize=(8*2,8*3))
ax = fig.add_subplot(3,2,1)
ax.hexbin(df['gmag'],df['spread_model_g'],gridsize=200,bins='log',mincnt=1,extent=(12,26,-0.05,0.05))
ax.set_xlabel('g',fontsize=20)
ax.set_ylabel('spread_model_g',fontsize=20)
ax.tick_params(labelsize=10)
ax2 = fig.add_subplot(3,2,2)
ax2.hexbin(df['kron_radius'],df['spread_model_g'],gridsize=200,bins='log',mincnt=1,extent=(0,10,-0.1,0.1))
ax2.set_xlabel('Kron radius',fontsize=20)
ax2.set_ylabel('spread_model_g',fontsize=20)
ax2.tick_params(labelsize=10)
ax3 = fig.add_subplot(3,2,3)
ax3.hexbin(df['gmag'],df['kron_radius'],gridsize=200,bins='log',mincnt=1,extent=(12,26,0,10))
ax3.set_xlabel('g',fontsize=20)
ax3.set_ylabel('Kron radius',fontsize=20)
ax3.tick_params(labelsize=10)
ax4 = fig.add_subplot(3,2,4)
ax4.hexbin(df['kron_radius'],df['class_star_g'],gridsize=200,bins='log',mincnt=1,extent=(0,10,0,1))
ax4.set_xlabel('Kron radius',fontsize=20)
ax4.set_ylabel('Class_star_g',fontsize=20)
ax4.tick_params(labelsize=10)
ax5 = fig.add_subplot(3,2,5)
ax5.hexbin(df['gmag'],df['class_star_g'],gridsize=200,bins='log',mincnt=1,extent=(12,26,0,1))
ax5.set_xlabel('g',fontsize=20)
ax5.set_ylabel('Class_star_g',fontsize=20)
ax5.tick_params(labelsize=10)
ax6 = fig.add_subplot(3,2,6)
ax6.hexbin(df['spread_model_g'],df['class_star_g'],gridsize=200,bins='log',mincnt=1,extent=(-0.025,0.025,0,1))
ax6.set_xlabel('spread_model_g',fontsize=20)
ax6.set_ylabel('Class_star_g',fontsize=20)
ax6.tick_params(labelsize=10)
The test above resulted in cleaner plots with fewer ridges on the class_star parameter.
Next, let's test what happens in cases with agreement or disagreement of the value of class_star between bands.
def plot_spread_mag(df_input,i,label=None,**kwargs):
ax = fig.add_subplot(2,2,i+1)
ax.hexbin(df_input['gmag'],df_input['spread_model_g'],gridsize=200,bins='log',mincnt=1,extent=(12,26,-0.05,0.05))
ax.set_xlabel('g',fontsize=20)
ax.set_ylabel('spread_model_g',fontsize=20)
ax.tick_params(labelsize=10)
if label is not None:
ax.text(0.1,0.1,label,transform=ax.transAxes,fontsize=18,color='red',backgroundcolor='white')
# reset df
df = df_all
# invalid mags are set to 99 -> mag_auto_X < 90
# spread_model_X is valid at abs(spread_model)<1
# require S/N>5 in the g, r, z band
df=df[(df['spread_model_g']>(-1)) & (df['spread_model_g']<1) & \
(df['snr_g']>5) & (df['snr_r']>5) & (df['snr_z']>5)]
df_gal = df[(df['class_star_g']<0.5) & (df['class_star_r']<0.5) & (df['class_star_z']<0.5)]
df_notok = df[(df['class_star_g']<0.5) & ((df['class_star_r']>0.5) | (df['class_star_z']>0.5))]
df_star = df[(df['class_star_g']>0.5) & (df['class_star_r']>0.5) & (df['class_star_z']>0.5)]
df_notok2 = df[(df['class_star_g']>0.5) & ((df['class_star_r']<0.5) | (df['class_star_z']<0.5))]
fig = plt.figure(figsize=(7*2,7*2))
plot_spread_mag(df_gal,0,label='Galaxies (good)')
plot_spread_mag(df_star,1,label='Stars (good)')
plot_spread_mag(df_notok,2,label='Galaxies (bad)')
plot_spread_mag(df_notok2,3,label='Stars (bad)')
outfile = 'class_star_g_TESTS.png'
fig.savefig(outfile, bbox_inches='tight')
6.3 Optical Color-Color Diagram with New Sample Definition¶
def plot_grz(df_input,i,label=None,**kwargs):
col0 = df_input['rmag'] - df_input['zmag'] #r-z color
col1 = df_input['gmag'] - df_input['rmag'] #g-r color
# 2D-histogram of objects
ax = fig.add_subplot(2,2,i+1)
ax.hexbin(col0, col1, bins='log', cmap=plt.cm.viridis, gridsize=140,
mincnt=1, extent=(-1., 3, -1., 3))
ax.set_xlabel('r-z',fontsize=20)
ax.set_ylabel('g-r',fontsize=20)
if label is not None:
ax.text(0.1,0.9,label,transform=ax.transAxes,fontsize=16,color='red',backgroundcolor='white')
#color bar
# cb = plt.colorbar(im1,label='log(N)')
fig = plt.figure(figsize=(7*2,7*2))
plot_grz(df_gal,0,label='Galaxies (good)')
plot_grz(df_star,1,label='Stars (good)')
plot_grz(df_notok,2,label='Galaxies (bad)')
plot_grz(df_notok2,3,label='Stars (bad)')
outfile = 'grz_DESDR1.png'
fig.savefig(outfile, bbox_inches='tight')
The galaxy branch and the stellar locus on the g-r vs. r-z plane are much cleaner. However, it is not clear what happens to the QSO's. It could be that they are mostly in the "cloud" below the branches and most obvious in the panel labeled "Stars (bad)". This means that they would look like a star in g but not in either r or z. At a fixed r-z, QSOs would be bluer than stars in g-r. One could investigate with spectroscopy or templates to confirm.
6.4 Comparison with LS + SDSS¶
For reference, this is a figure with LS (DECaLS) photometry for SDSS DR13 spectral classification (comparatively bright objects).
6.5 DES DR1 with g<22¶
# Repeat above with with gmag < 22 as in SDSS/BOSS DR12Q
g22_gal = df[(df['class_star_g']<0.5) & (df['class_star_r']<0.5) & (df['class_star_z']<0.5) & (df['gmag']<22.)]
g22_notok = df[(df['class_star_g']<0.5) & ((df['class_star_r']>0.5) | (df['class_star_z']>0.5)) & (df['gmag']<22.)]
g22_star = df[(df['class_star_g']>0.5) & (df['class_star_r']>0.5) & (df['class_star_z']>0.5) & (df['gmag']<22.)]
g22_notok2 = df[(df['class_star_g']>0.5) & ((df['class_star_r']<0.5) | (df['class_star_z']<0.5)) & (df['gmag']<22.)]
fig = plt.figure(figsize=(7*2,7*2))
plot_grz(g22_gal,0,label='Galaxies (good)')
plot_grz(g22_star,1,label='Stars (good)')
plot_grz(g22_notok,2,label='Galaxies (bad)')
plot_grz(g22_notok2,3,label='Stars (bad)')
outfile = 'grz_DESDR1_gLT22.png'
fig.savefig(outfile, bbox_inches='tight')
NOTES: In the panel above, the cloud of points near the bottom left tip of the stellar locus may include some quasars.
6.6 DES DR1 with g>23¶
# Repeat above with with gmag > 23 (1 mag fainter than SDSS/BOSS DR12Q)
g23_gal = df[(df['class_star_g']<0.5) & (df['class_star_r']<0.5) & (df['class_star_z']<0.5) & (df['gmag']>23.)]
g23_notok = df[(df['class_star_g']<0.5) & ((df['class_star_r']>0.5) | (df['class_star_z']>0.5)) & (df['gmag']>23.)]
g23_star = df[(df['class_star_g']>0.5) & (df['class_star_r']>0.5) & (df['class_star_z']>0.5) & (df['gmag']>23.)]
g23_notok2 = df[(df['class_star_g']>0.5) & ((df['class_star_r']<0.5) | (df['class_star_z']<0.5)) & (df['gmag']>23.)]
fig = plt.figure(figsize=(7*2,7*2))
plot_grz(g23_gal,0,label='Galaxies (good)')
plot_grz(g23_star,1,label='Stars (good)')
plot_grz(g23_notok,2,label='Galaxies (bad)')
plot_grz(g23_notok2,3,label='Stars (bad)')
outfile = 'grz_DESDR1_gGT23.png'
fig.savefig(outfile, bbox_inches='tight')
In the plot above, the stellar locus is broad and curved in a way that overlaps with galaxies. The increased scatter and increased overlap make using these colors for object identification more difficult compared to the situation for bright objects.
7 Comparison with DES Team classification¶
This section includes notes on a possible star-galaxy classification from the DES team. Please see the DES Data Release paper for the latest definition. It could be interesting to make a direct comparison with the categories above.
des_dr1.main is the table that corresponds to dr1 below:
Galaxies:
WHERE
dr1.WAVG_SPREAD_MODEL_I + 1.0*dr1.WAVG_SPREADERR_MODEL_I > 0.003 and
dr1.WAVG_SPREAD_MODEL_I - 1.0*dr1.WAVG_SPREADERR_MODEL_I > 0.001 and
dr1.WAVG_SPREAD_MODEL_I > -1 and
dr1.IMAFLAGS_ISO_I = 0 and
dr1.MAG_AUTO_I < 23
Stars:
WHERE
main.WAVG_SPREAD_MODEL_I + 3.0*main.WAVG_SPREADERR_MODEL_I < 0.005 and
main.WAVG_SPREAD_MODEL_I > -1 and
main.IMAFLAGS_ISO_I = 0 and
main.MAG_AUTO_I < 218 Extra: Class_star Comparison¶
We compare, for the 4 categories defined above, the class_star values between g and r bands, and between g and z bands.
def plot_class_star_gr(df_input,i,label=None,**kwargs):
ax = fig.add_subplot(2,4,i+1)
ax.hexbin(df_input['class_star_r'],df_input['class_star_g'],gridsize=200,bins='log',mincnt=1,extent=(0,1,0,1.2))
ax.set_xlabel('Class_star_r',fontsize=10)
ax.set_ylabel('Class_star_g',fontsize=10)
ax.tick_params(labelsize=10)
if label is not None:
ax.text(0.1,0.9,label,transform=ax.transAxes,fontsize=16,color='red',backgroundcolor='white')
def plot_class_star_gz(df_input,i,label=None,**kwargs):
ax = fig.add_subplot(2,4,i+1)
ax.hexbin(df_input['class_star_z'],df_input['class_star_g'],gridsize=200,bins='log',mincnt=1,extent=(0,1,0,1.2))
ax.set_xlabel('Class_star_z',fontsize=10)
ax.set_ylabel('Class_star_g',fontsize=10)
ax.tick_params(labelsize=10)
if label is not None:
ax.text(0.1,0.9,label,transform=ax.transAxes,fontsize=16,color='red',backgroundcolor='white')
fig = plt.figure(figsize=(4*4,4.5*2))
plot_class_star_gr(df_gal,0,label='Galaxies (good)')
plot_class_star_gr(df_star,1,label='Stars (good)')
plot_class_star_gr(df_notok,2,label='Galaxies (bad)')
plot_class_star_gr(df_notok2,3,label='Stars (bad)')
plot_class_star_gz(df_gal,4,label='Galaxies (good)')
plot_class_star_gz(df_star,5,label='Stars (good)')
plot_class_star_gz(df_notok,6,label='Galaxies (bad)')
plot_class_star_gz(df_notok2,7,label='Stars (bad)')
outfile = 'class_star_g_COMPARE.png'
fig.savefig(outfile, bbox_inches='tight')
9 References¶
Soumagnac et al (2015)
Desai et al (2012)